Nucleotide diversity
Nucleotide diversity (often referred to using the symbol π) is the
average pairwise difference between all possible pairs of individuals in
your sample. It is a very intuitive and simple measure of genetic
diversity, and is accurately estimated even with very few samples. A
formal definition is here.
We can obtain the nucleotide diversity (Ï€) from our VCF file using
vcftools software. In our case we will collect the π value from each 10
kb (10,000 bp) window of the genome.
NB: vcftools is a very flexible tool for analyzing, manipulating VCF
files. It can do many other wonderful things. The vcftools manual is on
github here (https://vcftools.sourceforge.net/man_latest.html).
Breaking up pi by treatment type?
I believe that an important step would be to compare nucleotide
diversity between the different treatment groups. The following code
present information for all treatment groups and compares it to each
individual treatment group.
Start of modified workflow
Setup
The following was run on the command line
# Make ROD_CADO_working directory in home
mkdir ROD_CADO_working
cd ROD_CADO_working
# Make Nucleotide_diversity directory
mkdir ROD_CADO_working
# create popmap file with sample and treatment names
cp /home/Shared_Data/ROD_CADO/analysis/popmap popmap
# manually add in treatment names to popmap file (w/ code)
head treat_popmap
C1_2 con-con C1_4 con-con C1_5 con-con C1_6 con-con C1_7 con-con C1_8
con-con C1_9 con-con C2_10 con-con C2_11 con-con C2_2 con-con
Subsetting popmap groups
Create treatment specific files containing a single column of all of
the sample names within that treatment
awk '$2 == "con-con" {print $1}' treat_popmap > con-con.txt | awk '$2 == "str-con" {print $1}' treat_popmap > str-con.txt | awk '$2 == "con-rod" {print $1}' treat_popmap > con-rod.txt | awk '$2 == "str-rod" {print $1}' treat_popmap > str-rod.txt
Make script with loop using ChatGPT
Make script
#!/bin/bash
VCF=SNP.TRSdp10g1.FIL.vcf.gz
POPS=("con-con" "str-con" "con-rod" "str-rod")
for POP in "${POPS[@]}"; do
echo "Processing $POP..."
KEEP="popmap_files/${POP}.txt"
OUT_PREFIX="temp_${POP//-}"
REC_VCF="${OUT_PREFIX}.recode.vcf"
REC_VCFGZ="${REC_VCF}.gz"
OUTPUT_PI="ROD.CADO.${POP}.pi.windowed.pi"
# Step 1: Filter and recode
vcftools --gzvcf "$VCF" \
--keep "$KEEP" \
--recode --recode-INFO-all \
--out "$OUT_PREFIX"
# Step 2: Compress VCF and remove uncompressed
bgzip "$REC_VCF"
rm "$REC_VCF"
# Step 3: Calculate windowed pi
vcftools --gzvcf "$REC_VCFGZ" \
--window-pi 10000 \
--out "$OUTPUT_PI"
# Step 4: Clean up compressed VCF
rm "$REC_VCFGZ"
echo "Finished processing $POP"
echo "---------------------------"
done
Make executable
chmod +x run_pi_calculations.sh
Run in tmux
# Run in tmux
tmux new -s pi_calc
# Reattach later
tmux attach-session -t pi_calc
Load dataframe
pi.all.dataframe<-read.table("/home/Shared_Data/ROD_CADO/analysis/raw.vcf/ROD.CADO.all.pi.windowed.pi", sep="\t", header=T)
pi.concon.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.con-con.pi.windowed.pi.windowed.pi", sep="\t", header=T)
pi.conrod.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.con-rod.pi.windowed.pi.windowed.pi", sep="\t", header=T)
pi.strcon.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.str-con.pi.windowed.pi.windowed.pi", sep="\t", header=T)
pi.strrod.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.str-rod.pi.windowed.pi.windowed.pi", sep="\t", header=T)
Color palette
#Here is the color pallette that we will use for everything:
col_pal <- c("#0072B2", "#56B4E9", "#E69F00", "#F0E442")
#Let's factor treatments as follows:
df$TREAT <- factor(df$TREAT, levels=c("CONCON", "STRCON", "CONROD", "STRROD"))
Modify CHROM column in dataframe
pi.all.dataframe %>%
mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10")) -> pi.all.df
pi.all.df$CHROM <- as.factor(pi.all.df$CHROM)
pi.concon.dataframe %>%
mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10")) -> pi.concon.df
pi.concon.df$CHROM <- as.factor(pi.concon.df$CHROM)
pi.conrod.dataframe %>%
mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10")) -> pi.conrod.df
pi.conrod.df$CHROM <- as.factor(pi.conrod.df$CHROM)
pi.strcon.dataframe %>%
mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10")) -> pi.strcon.df
pi.strcon.df$CHROM <- as.factor(pi.strcon.df$CHROM)
pi.strrod.dataframe %>%
mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>%
mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10")) -> pi.strrod.df
pi.strrod.df$CHROM <- as.factor(pi.strrod.df$CHROM)
For loop to replace previous dataframe manipulation
# Create named vector to map chromosome names
chrom_map <- setNames(as.character(1:10), paste0("NC_03578", 0:9, ".1"))
# List of original dataframe names (as strings)
input_names <- c(
"pi.all.dataframe",
"pi.concon.dataframe",
"pi.conrod.dataframe",
"pi.strcon.dataframe",
"pi.strrod.dataframe"
)
# Corresponding output dataframe names
output_names <- c(
"pi.all.df",
"pi.concon.df",
"pi.conrod.df",
"pi.strcon.df",
"pi.strrod.df"
)
# Loop through each dataframe
for (i in seq_along(input_names)) {
df <- get(input_names[i]) # retrieve the dataframe by name
# Replace chromosome names
for (old in names(chrom_map)) {
df <- df %>% mutate(CHROM = str_replace(CHROM, old, chrom_map[[old]]))
}
# Convert to factor
df$CHROM <- as.factor(df$CHROM)
# Assign to new name in global environment
assign(output_names[i], df)
}
Descriptive statistics
summary(pi.all.df)
by(pi.all.df, pi.all.df$CHROM, summary)
cor(pi.all.df$N_VARIANTS, pi.all.df$PI)
summary(pi.concon.df)
by(pi.concon.df, pi.concon.df$CHROM, summary)
cor(pi.concon.df$N_VARIANTS, pi.concon.df$PI)
summary(pi.conrod.df)
by(pi.conrod.df, pi.conrod.df$CHROM, summary)
cor(pi.conrod.df$N_VARIANTS, pi.conrod.df$PI)
summary(pi.strcon.df)
by(pi.strcon.df, pi.strcon.df$CHROM, summary)
cor(pi.strcon.df$N_VARIANTS, pi.strcon.df$PI)
summary(pi.strrod.df)
by(pi.strrod.df, pi.strrod.df$CHROM, summary)
cor(pi.strrod.df$N_VARIANTS, pi.strrod.df$PI)
New loop and plotting for statistics
col_pal <- c(
"ALL" = "gray70",
"CONCON" = "#0072B2",
"STRCON" = "#56B4E9",
"CONROD" = "#E69F00",
"STRROD" = "#F0E442"
)
df_names <- c("pi.all.df", "pi.concon.df", "pi.strcon.df", "pi.conrod.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "STRCON", "CONROD", "STRROD")
chrom_levels <- as.character(1:10)
summary_list <- list()
for (i in seq_along(df_names)) {
df <- get(df_names[i])
treat <- df_labels[i]
df$TREAT <- factor(treat, levels = names(col_pal))
chrom_summary <- df %>%
group_by(CHROM, TREAT) %>%
summarise(
mean_PI = mean(PI, na.rm = TRUE),
se_PI = sd(PI, na.rm = TRUE) / sqrt(n()),
mean_N_VARIANTS = mean(N_VARIANTS, na.rm = TRUE),
se_N_VARIANTS = sd(N_VARIANTS, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
) %>%
mutate(CHROM = factor(CHROM, levels = chrom_levels))
summary_list[[i]] <- chrom_summary
}
summary_df <- bind_rows(summary_list)
mean_pi_plot <- ggplot(summary_df, aes(x = CHROM, y = mean_PI, fill = TREAT)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
geom_errorbar(aes(ymin = mean_PI - se_PI, ymax = mean_PI + se_PI),
position = position_dodge(width = 0.8), width = 0.2) +
scale_fill_manual(values = col_pal, name = "Treatment") +
labs(title = "Mean PI per Chromosome", x = "Chromosome", y = "Mean PI") +
theme_minimal()
mean_n_plot <- ggplot(summary_df, aes(x = CHROM, y = mean_N_VARIANTS, fill = TREAT)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
geom_errorbar(aes(ymin = mean_N_VARIANTS - se_N_VARIANTS, ymax = mean_N_VARIANTS + se_N_VARIANTS),
position = position_dodge(width = 0.8), width = 0.2) +
scale_fill_manual(values = col_pal, name = "Treatment") +
labs(title = "Mean number of variants per Chromosome", x = "Chromosome", y = "Mean # variants") +
theme_minimal()
print(mean_pi_plot)


print(mean_n_plot)
ggsave("mean_pi_plot.png", plot = mean_pi_plot, width = 10, height = 6, dpi = 300)
ggsave("mean_n_variants_plot.png", plot = mean_n_plot, width = 10, height = 6, dpi = 300)

Correlation visualizations
for (i in seq_along(df_names)) {
df <- get(df_names[i])
label <- df_labels[i]
p <- ggplot(df, aes(x = N_VARIANTS, y = PI)) +
geom_point(alpha = 0.4) +
geom_smooth(method = "lm", se = FALSE, color = "blue") +
labs(title = paste("Correlation: PI vs # variants —", label),
x = "N_VARIANTS",
y = "PI") +
theme_minimal()
print(p)
}





Table of correlations
cor_table <- tibble(
dataset = df_labels,
correlation = map_dbl(df_names, ~ cor(get(.x)$N_VARIANTS, get(.x)$PI, use = "complete.obs"))
)
print(cor_table)
Plot PI by chromosome
ggplot(pi.all.df, aes(x=CHROM, y=PI,))+
geom_violin(aes(color=CHROM,fill=CHROM))+
geom_boxplot(aes(fill=CHROM), width=0.1,outlier.shape = 23, outlier.color = "black")+
stat_summary(fun=mean, geom="point", shape=23, size=2)+
scale_fill_brewer(palette = "Paired")+
theme_classic()
Plot PI by chromosome loop
# List of dataframes and labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("All", "ConCon", "ConRod", "StrCon", "StrRod")
# Standard chromosome order
chrom_levels <- as.character(1:10)
# Combine all into one dataframe
combined_df <- purrr::map2_dfr(df_names, df_labels, function(df_name, label) {
df <- get(df_name)
df %>%
mutate(
dataset = label,
CHROM = factor(CHROM, levels = chrom_levels)
)
})
# Faceted violin + boxplot
ggplot(combined_df, aes(x = CHROM, y = PI)) +
geom_violin(aes(color = CHROM, fill = CHROM), trim = FALSE) +
geom_boxplot(aes(fill = CHROM), width = 0.1, outlier.shape = 23, outlier.color = "black") +
stat_summary(fun = mean, geom = "point", shape = 23, size = 2) +
scale_fill_brewer(palette = "Paired") +
labs(title = "PI Distribution by Chromosome (Faceted by Dataset)",
x = "Chromosome", y = "PI") +
facet_wrap(~ dataset, ncol = 2) +
theme_classic() +
theme(legend.position = "none")

Smaller visualizations
hist(mydf$PI,br=40)
boxplot(mydf$PI, ylab="Nuc Diversity")
Plot By position
ggplot(pi.all.df, aes(x=BIN_START, y=PI, color=CHROM))+
geom_point()+
scale_fill_brewer(palette = "Paired") +
scale_x_continuous(labels = label_number(scale = 1e-6, suffix = "M")) +
facet_wrap(~CHROM)+
theme_classic()
Loop for plot by position
# Define dataframe names and labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "CONROD", "STRCON", "STRROD")
# Get global PI range
all_pi_values <- unlist(lapply(df_names, function(x) get(x)$PI))
global_ymin <- min(all_pi_values, na.rm = TRUE)
global_ymax <- max(all_pi_values, na.rm = TRUE)
# Loop over dataframes
for (i in seq_along(df_names)) {
df <- get(df_names[i])
label <- df_labels[i]
# Ensure CHROM is a factor ordered from 1 to 10
df$CHROM <- factor(df$CHROM, levels = as.character(1:10))
p <- ggplot(df, aes(x = BIN_START, y = PI, color = CHROM)) +
geom_point() +
facet_wrap(~CHROM) +
scale_fill_brewer(palette = "Paired") +
scale_x_continuous(labels = label_number(scale = 1e-6, suffix = "M")) + # Human readable x-axis
ylim(global_ymin, global_ymax) + # Same y-axis for all plots
theme_classic() +
labs(title = paste("PI vs BIN_START -", label),
x = "BIN_START (millions)",
y = "PI")
print(p)
ggsave(filename = paste0("PI_vs_BIN_START_", label, ".png"),
plot = p, width = 10, height = 6, dpi = 300)
}





Facet wrap chromosome showing them across different treatment
types
# Define dataframe names and labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "CONROD", "STRCON", "STRROD")
# Custom color palette
col_pal <- c(
"ALL" = "gray70",
"CONCON" = "#0072B2",
"STRCON" = "#56B4E9",
"CONROD" = "#E69F00",
"STRROD" = "#F0E442"
)
# Combine all data into one dataframe with treatment labels
all_data <- bind_rows(lapply(seq_along(df_names), function(i) {
df <- get(df_names[i])
df$Treatment <- df_labels[i]
df
}))
# Set CHROM and Treatment as ordered factors
all_data$CHROM <- factor(all_data$CHROM, levels = as.character(1:10))
all_data$Treatment <- factor(all_data$Treatment, levels = df_labels)
# Get global PI range
global_ymin <- min(all_data$PI, na.rm = TRUE)
global_ymax <- max(all_data$PI, na.rm = TRUE)
# Loop through chromosomes 1 to 10
for (chr in 1:10) {
chr_str <- as.character(chr)
chr_data <- filter(all_data, CHROM == chr_str)
p <- ggplot(chr_data, aes(x = BIN_START, y = PI, color = Treatment)) +
geom_point(alpha = 0.6, size = 0.5) +
facet_wrap(~Treatment, nrow = 1) +
scale_color_manual(values = col_pal) +
scale_x_continuous(labels = label_number(scale = 1e-6, suffix = "M")) +
ylim(global_ymin, global_ymax) +
theme_classic() +
labs(
title = paste("Chromosome", chr, "- PI across Treatment Types"),
x = "BIN_START (millions)",
y = "PI"
)
print(p)
ggsave(
filename = paste0("PI_chr", chr, "_across_treatments.png"),
plot = p,
width = 16,
height = 4,
dpi = 300
)
}










Only chromosome 1
# Subset by chrom
mydf.chr1 <- mydf[which(mydf$CHROM=="1"),]
ggplot(mydf.chr1, aes(x=BIN_START, y=PI))+
geom_point()+
theme_classic()
# List of treatment data frames and their labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "CONROD", "STRCON", "STRROD")
# Step 1: Calculate global PI range across all dataframes
all_pi_values <- unlist(lapply(df_names, function(x) get(x)$PI))
global_ymin <- min(all_pi_values, na.rm = TRUE)
global_ymax <- max(all_pi_values, na.rm = TRUE)
# Step 2: Create plots and save as PNGs
for (j in seq_along(df_names)) {
df <- get(df_names[j])
label <- df_labels[j]
for (i in 1:10) {
chr_data <- df[df$CHROM == as.character(i), ]
p <- ggplot(chr_data, aes(x = BIN_START, y = PI)) +
geom_point() +
theme_classic() +
ggtitle(paste("Treatment:", label, "- Chromosome", i)) +
labs(x = "BIN_START", y = "PI") +
ylim(global_ymin, global_ymax)
filename <- paste0("PI_", label, "_chr", i, ".png")
ggsave(filename = filename, plot = p, width = 8, height = 5, dpi = 300)
}
}
---
title: "Genetic Diversity work"
output:
  html_notebook:
    fig_caption: yes
    toc: yes
    toc_depth: 3
    toc_float: yes
  pdf_document:
    toc: yes
    toc_depth: '3'
  html_document:
    keep_md: TRUE
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, root_dir = "/home/Shared_Data/ROD_CADO/analysis/raw.vcf/filtered")
```

# Load libraries and file paths
```{r, message=FALSE, warning=FALSE}
library("plyr")
library("dplyr")
library("ggplot2")
library(R.utils)
library(gghighlight)
library(ggman)
library(ggtext)
library(patchwork)
library(plotrix)
library(qqman)
library(qvalue)
library(reshape2)
library(tidyr)
library(zoo)
library(infer)
options(dplyr.summarise.inform = FALSE)
library(bigsnpr)
library("wesanderson")
library("directlabels")
library(OutFLANK)
library(adegenet)
library(poppr)
library(vcfR)
library(stringr)
library(matrixStats)
library(purrr)
library(scales) 
```

# Nucleotide diversity

Nucleotide diversity (often referred to using the symbol π) is the average pairwise difference between all possible pairs of individuals in your sample. It is a very intuitive and simple measure of genetic diversity, and is accurately estimated even with very few samples. A formal definition is here.

We can obtain the nucleotide diversity (π) from our VCF file using vcftools software. In our case we will collect the π value from each 10 kb (10,000 bp) window of the genome.

NB: vcftools is a very flexible tool for analyzing, manipulating VCF files. It can do many other wonderful things. The vcftools manual is on github here (https://vcftools.sourceforge.net/man_latest.html).

### Breaking up pi by treatment type?

I believe that an important step would be to compare nucleotide diversity between the different treatment groups. The following code present information for all treatment groups and compares it to each individual treatment group.

## Start of modified workflow

## Setup 

The following was run on the command line
```{bash}
# Make ROD_CADO_working directory in home
mkdir ROD_CADO_working
cd ROD_CADO_working
# Make Nucleotide_diversity directory
mkdir ROD_CADO_working
# create popmap file with sample and treatment names
cp /home/Shared_Data/ROD_CADO/analysis/popmap popmap
# manually add in treatment names to popmap file (w/ code)
head treat_popmap 
```
C1_2    con-con
C1_4    con-con
C1_5    con-con
C1_6    con-con
C1_7    con-con
C1_8    con-con
C1_9    con-con
C2_10   con-con
C2_11   con-con
C2_2    con-con

# Subsetting popmap groups
Create treatment specific files containing a single column of all of the sample names within that treatment
```{bash}
awk '$2 == "con-con" {print $1}' treat_popmap > con-con.txt | awk '$2 == "str-con" {print $1}' treat_popmap > str-con.txt | awk '$2 == "con-rod" {print $1}' treat_popmap > con-rod.txt | awk '$2 == "str-rod" {print $1}' treat_popmap > str-rod.txt
```

### Run VCF tools PI window
```{bash eval = FALSE}
#bcftools view --threads 20 -S SNP.TRSdp10g1.FIL.vcf | vcftools --vcf -  --window-pi 10000 --out ROD.CADO.all.pi

# For con-con
# Step 1: Filter VCF for population subset
vcftools --gzvcf SNP.TRSdp10g1.FIL.vcf.gz --keep con-con.txt --recode --recode-INFO-all --out temp_concon_filtered

# Step 2: bgzip output
bgzip temp_concon_filtered.recode.vcf

# Step 3: Calculate windowed pi
vcftools --gzvcf temp_concon.filtered.recode.vcf.gz --window-pi 10000 --out ROD.CADO.con-con.pi.windowed.pi

# For str-con
# Step 1: Filter VCF for population subset
vcftools --gzvcf SNP.TRSdp10g1.FIL.vcf.gz --keep popmap_files/str-con.txt --recode --recode-INFO-all --out temp_strcon_filtered

# Step 2: bgzip output
bgzip temp_strcon_filtered.recode.vcf

# Step 3: # Step 3: Calculate windowed pi
vcftools --gzvcf temp_strcon_filtered.recode.vcf.gz --window-pi 10000 --out ROD.CADO.str-con.pi.windowed.pi

# For con-rod
# Step 1: Filter VCF for population subset
vcftools --gzvcf SNP.TRSdp10g1.FIL.vcf --keep popmap_files/con-rod.txt --recode --recode-INFO-all --out temp_conrod_filtered

# Step 2: bgzip output
bgzip temp_conrod_filtered.recode.vcf

# Step 3: Calculate windowed pi
vcftools --gzvcf temp_conrod_filtered.recode.vcf.gz --window-pi 10000 --out ROD.CADO.con-rod.pi.windowed.pi

# For str-rod
# Step 1: Filter VCF for population subset
vcftools --gzvcf SNP.TRSdp10g1.FIL.vcf --keep popmap_files/str-rod.txt --recode --recode-INFO-all --out temp_strrod_filtered

# Step 2: bgzip output
bgzip temp_strrod_filtered.recode.vcf

# Step 3: Calculate windowed pi
vcftools --gzvcf temp_strrod_filtered.recode.vcf.gz --window-pi 10000 --out ROD.CADO.str-rod.pi.windowed.pi
```

### Make script with loop using ChatGPT

#### Make script

```{bash}
#!/bin/bash

VCF=SNP.TRSdp10g1.FIL.vcf.gz
POPS=("con-con" "str-con" "con-rod" "str-rod")

for POP in "${POPS[@]}"; do
    echo "Processing $POP..."

    KEEP="popmap_files/${POP}.txt"
    OUT_PREFIX="temp_${POP//-}"
    REC_VCF="${OUT_PREFIX}.recode.vcf"
    REC_VCFGZ="${REC_VCF}.gz"
    OUTPUT_PI="ROD.CADO.${POP}.pi.windowed.pi"

    # Step 1: Filter and recode
    vcftools --gzvcf "$VCF" \
        --keep "$KEEP" \
        --recode --recode-INFO-all \
        --out "$OUT_PREFIX"

    # Step 2: Compress VCF and remove uncompressed
    bgzip "$REC_VCF"
    rm "$REC_VCF"

    # Step 3: Calculate windowed pi
    vcftools --gzvcf "$REC_VCFGZ" \
        --window-pi 10000 \
        --out "$OUTPUT_PI"

    # Step 4: Clean up compressed VCF
    rm "$REC_VCFGZ"

    echo "Finished processing $POP"
    echo "---------------------------"
done
```

#### Make executable

```{bash}
chmod +x run_pi_calculations.sh
```

#### Run in tmux
```{bash}
# Run in tmux
tmux new -s pi_calc
# Reattach later
tmux attach-session -t pi_calc
```

### Load dataframe
```{r, message=FALSE, warning=FALSE}
pi.all.dataframe<-read.table("/home/Shared_Data/ROD_CADO/analysis/raw.vcf/ROD.CADO.all.pi.windowed.pi", sep="\t", header=T)
pi.concon.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.con-con.pi.windowed.pi.windowed.pi", sep="\t", header=T)
pi.conrod.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.con-rod.pi.windowed.pi.windowed.pi", sep="\t", header=T)
pi.strcon.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.str-con.pi.windowed.pi.windowed.pi", sep="\t", header=T)
pi.strrod.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.str-rod.pi.windowed.pi.windowed.pi", sep="\t", header=T)
```

### Color palette
```{r}
#Here is the color pallette that we will use for everything:

col_pal <- c("#0072B2", "#56B4E9", "#E69F00", "#F0E442")

#Let's factor treatments as follows:

df$TREAT <- factor(df$TREAT, levels=c("CONCON", "STRCON", "CONROD", "STRROD"))
```

### Modify CHROM column in dataframe
```{r, message=FALSE, warning=FALSE}
pi.all.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.all.df
pi.all.df$CHROM <- as.factor(pi.all.df$CHROM)

pi.concon.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.concon.df
pi.concon.df$CHROM <- as.factor(pi.concon.df$CHROM)

pi.conrod.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.conrod.df
pi.conrod.df$CHROM <- as.factor(pi.conrod.df$CHROM)

pi.strcon.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.strcon.df
pi.strcon.df$CHROM <- as.factor(pi.strcon.df$CHROM)

pi.strrod.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.strrod.df
pi.strrod.df$CHROM <- as.factor(pi.strrod.df$CHROM)
```

### For loop to replace previous dataframe manipulation

```{r, message=FALSE, warning=FALSE}

# Create named vector to map chromosome names
chrom_map <- setNames(as.character(1:10), paste0("NC_03578", 0:9, ".1"))

# List of original dataframe names (as strings)
input_names <- c(
  "pi.all.dataframe",
  "pi.concon.dataframe",
  "pi.conrod.dataframe",
  "pi.strcon.dataframe",
  "pi.strrod.dataframe"
)

# Corresponding output dataframe names
output_names <- c(
  "pi.all.df",
  "pi.concon.df",
  "pi.conrod.df",
  "pi.strcon.df",
  "pi.strrod.df"
)

# Loop through each dataframe
for (i in seq_along(input_names)) {
  df <- get(input_names[i])  # retrieve the dataframe by name
  
  # Replace chromosome names
  for (old in names(chrom_map)) {
    df <- df %>% mutate(CHROM = str_replace(CHROM, old, chrom_map[[old]]))
  }
  
  # Convert to factor
  df$CHROM <- as.factor(df$CHROM)
  
  # Assign to new name in global environment
  assign(output_names[i], df)
}
```


### Descriptive statistics
```{r}
summary(pi.all.df)
by(pi.all.df, pi.all.df$CHROM, summary)
cor(pi.all.df$N_VARIANTS, pi.all.df$PI)

summary(pi.concon.df)
by(pi.concon.df, pi.concon.df$CHROM, summary)
cor(pi.concon.df$N_VARIANTS, pi.concon.df$PI)

summary(pi.conrod.df)
by(pi.conrod.df, pi.conrod.df$CHROM, summary)
cor(pi.conrod.df$N_VARIANTS, pi.conrod.df$PI)

summary(pi.strcon.df)
by(pi.strcon.df, pi.strcon.df$CHROM, summary)
cor(pi.strcon.df$N_VARIANTS, pi.strcon.df$PI)

summary(pi.strrod.df)
by(pi.strrod.df, pi.strrod.df$CHROM, summary)
cor(pi.strrod.df$N_VARIANTS, pi.strrod.df$PI)
```

### New loop and plotting for statistics

```{r}
col_pal <- c(
  "ALL" = "gray70",
  "CONCON" = "#0072B2", 
  "STRCON" = "#56B4E9", 
  "CONROD" = "#E69F00", 
  "STRROD" = "#F0E442"
)

df_names <- c("pi.all.df", "pi.concon.df", "pi.strcon.df", "pi.conrod.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "STRCON", "CONROD", "STRROD")
chrom_levels <- as.character(1:10)

summary_list <- list()

for (i in seq_along(df_names)) {
  df <- get(df_names[i])
  treat <- df_labels[i]
  
  df$TREAT <- factor(treat, levels = names(col_pal))
  
  chrom_summary <- df %>%
    group_by(CHROM, TREAT) %>%
    summarise(
      mean_PI = mean(PI, na.rm = TRUE),
      se_PI = sd(PI, na.rm = TRUE) / sqrt(n()),
      mean_N_VARIANTS = mean(N_VARIANTS, na.rm = TRUE),
      se_N_VARIANTS = sd(N_VARIANTS, na.rm = TRUE) / sqrt(n()),
      .groups = "drop"
    ) %>%
    mutate(CHROM = factor(CHROM, levels = chrom_levels))
  
  summary_list[[i]] <- chrom_summary
}

summary_df <- bind_rows(summary_list)

mean_pi_plot <- ggplot(summary_df, aes(x = CHROM, y = mean_PI, fill = TREAT)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
  geom_errorbar(aes(ymin = mean_PI - se_PI, ymax = mean_PI + se_PI),
                position = position_dodge(width = 0.8), width = 0.2) +
  scale_fill_manual(values = col_pal, name = "Treatment") +
  labs(title = "Mean PI per Chromosome", x = "Chromosome", y = "Mean PI") +
  theme_minimal()

mean_n_plot <- ggplot(summary_df, aes(x = CHROM, y = mean_N_VARIANTS, fill = TREAT)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
  geom_errorbar(aes(ymin = mean_N_VARIANTS - se_N_VARIANTS, ymax = mean_N_VARIANTS + se_N_VARIANTS),
                position = position_dodge(width = 0.8), width = 0.2) +
  scale_fill_manual(values = col_pal, name = "Treatment") +
  labs(title = "Mean number of variants per Chromosome", x = "Chromosome", y = "Mean # variants") +
  theme_minimal()

print(mean_pi_plot)
print(mean_n_plot)

ggsave("mean_pi_plot.png", plot = mean_pi_plot, width = 10, height = 6, dpi = 300)
ggsave("mean_n_variants_plot.png", plot = mean_n_plot, width = 10, height = 6, dpi = 300)

```

# Correlation visualizations
```{r}
for (i in seq_along(df_names)) {
  df <- get(df_names[i])
  label <- df_labels[i]
  
  p <- ggplot(df, aes(x = N_VARIANTS, y = PI)) +
    geom_point(alpha = 0.4) +
    geom_smooth(method = "lm", se = FALSE, color = "blue") +
    labs(title = paste("Correlation: PI vs # variants —", label),
         x = "N_VARIANTS",
         y = "PI") +
    theme_minimal()
  
  print(p)
}
```

### Table of correlations

```{r}
cor_table <- tibble(
  dataset = df_labels,
  correlation = map_dbl(df_names, ~ cor(get(.x)$N_VARIANTS, get(.x)$PI, use = "complete.obs"))
)

print(cor_table)
```

### Plot PI by chromosome
```{r, message=FALSE, warning=FALSE}
ggplot(pi.all.df, aes(x=CHROM, y=PI,))+
  geom_violin(aes(color=CHROM,fill=CHROM))+
  geom_boxplot(aes(fill=CHROM), width=0.1,outlier.shape = 23, outlier.color = "black")+
  stat_summary(fun=mean, geom="point", shape=23, size=2)+
  scale_fill_brewer(palette = "Paired")+
  theme_classic()
```

### Plot PI by chromosome loop

```{r}
# List of dataframes and labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("All", "ConCon", "ConRod", "StrCon", "StrRod")

# Standard chromosome order
chrom_levels <- as.character(1:10)

# Combine all into one dataframe
combined_df <- purrr::map2_dfr(df_names, df_labels, function(df_name, label) {
  df <- get(df_name)
  df %>%
    mutate(
      dataset = label,
      CHROM = factor(CHROM, levels = chrom_levels)
    )
})

# Faceted violin + boxplot
ggplot(combined_df, aes(x = CHROM, y = PI)) +
  geom_violin(aes(color = CHROM, fill = CHROM), trim = FALSE) +
  geom_boxplot(aes(fill = CHROM), width = 0.1, outlier.shape = 23, outlier.color = "black") +
  stat_summary(fun = mean, geom = "point", shape = 23, size = 2) +
  scale_fill_brewer(palette = "Paired") +
  labs(title = "PI Distribution by Chromosome (Faceted by Dataset)",
       x = "Chromosome", y = "PI") +
  facet_wrap(~ dataset, ncol = 2) +
  theme_classic() +
  theme(legend.position = "none")

```

### Smaller visualizations
```{r, message=FALSE, warning=FALSE}
hist(mydf$PI,br=40)

boxplot(mydf$PI, ylab="Nuc Diversity")
```

### Plot By position
```{r, message=FALSE, warning=FALSE}
ggplot(pi.all.df, aes(x=BIN_START, y=PI, color=CHROM))+
  geom_point()+
  scale_fill_brewer(palette = "Paired") +
  scale_x_continuous(labels = label_number(scale = 1e-6, suffix = "M")) +
  facet_wrap(~CHROM)+
  theme_classic()
```
### Loop for plot by position
```{r}
# Define dataframe names and labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "CONROD", "STRCON", "STRROD")

# Get global PI range
all_pi_values <- unlist(lapply(df_names, function(x) get(x)$PI))
global_ymin <- min(all_pi_values, na.rm = TRUE)
global_ymax <- max(all_pi_values, na.rm = TRUE)

# Loop over dataframes
for (i in seq_along(df_names)) {
  df <- get(df_names[i])
  label <- df_labels[i]
  
  # Ensure CHROM is a factor ordered from 1 to 10
  df$CHROM <- factor(df$CHROM, levels = as.character(1:10))
  
  p <- ggplot(df, aes(x = BIN_START, y = PI, color = CHROM)) +
    geom_point() +
    facet_wrap(~CHROM) +
    scale_fill_brewer(palette = "Paired") +
    scale_x_continuous(labels = label_number(scale = 1e-6, suffix = "M")) +  # Human readable x-axis
    ylim(global_ymin, global_ymax) +  # Same y-axis for all plots
    theme_classic() +
    labs(title = paste("PI vs BIN_START -", label),
         x = "BIN_START (millions)",
         y = "PI")
  
  print(p)
  
  ggsave(filename = paste0("PI_vs_BIN_START_", label, ".png"),
         plot = p, width = 10, height = 6, dpi = 300)
}

```

### Facet wrap chromosome showing them across different treatment types
```{r}
# Define dataframe names and labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "CONROD", "STRCON", "STRROD")

# Custom color palette
col_pal <- c(
  "ALL" = "gray70",
  "CONCON" = "#0072B2", 
  "STRCON" = "#56B4E9", 
  "CONROD" = "#E69F00", 
  "STRROD" = "#F0E442"
)

# Combine all data into one dataframe with treatment labels
all_data <- bind_rows(lapply(seq_along(df_names), function(i) {
  df <- get(df_names[i])
  df$Treatment <- df_labels[i]
  df
}))

# Set CHROM and Treatment as ordered factors
all_data$CHROM <- factor(all_data$CHROM, levels = as.character(1:10))
all_data$Treatment <- factor(all_data$Treatment, levels = df_labels)

# Get global PI range
global_ymin <- min(all_data$PI, na.rm = TRUE)
global_ymax <- max(all_data$PI, na.rm = TRUE)

# Loop through chromosomes 1 to 10
for (chr in 1:10) {
  chr_str <- as.character(chr)
  chr_data <- filter(all_data, CHROM == chr_str)
  
  p <- ggplot(chr_data, aes(x = BIN_START, y = PI, color = Treatment)) +
    geom_point(alpha = 0.6, size = 0.5) +
    facet_wrap(~Treatment, nrow = 1) +
    scale_color_manual(values = col_pal) +
    scale_x_continuous(labels = label_number(scale = 1e-6, suffix = "M")) +
    ylim(global_ymin, global_ymax) +
    theme_classic() +
    labs(
      title = paste("Chromosome", chr, "- PI across Treatment Types"),
      x = "BIN_START (millions)",
      y = "PI"
    )
  
  print(p)
  
  ggsave(
    filename = paste0("PI_chr", chr, "_across_treatments.png"),
    plot = p,
    width = 16,
    height = 4,
    dpi = 300
  )
}

```
### Only chromosome 1
```{r, message=FALSE, warning=FALSE}
# Subset by chrom
mydf.chr1 <- mydf[which(mydf$CHROM=="1"),]

ggplot(mydf.chr1, aes(x=BIN_START, y=PI))+
  geom_point()+
  theme_classic()
```

```{r}
# List of treatment data frames and their labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "CONROD", "STRCON", "STRROD")

# Step 1: Calculate global PI range across all dataframes
all_pi_values <- unlist(lapply(df_names, function(x) get(x)$PI))
global_ymin <- min(all_pi_values, na.rm = TRUE)
global_ymax <- max(all_pi_values, na.rm = TRUE)

# Step 2: Create plots and save as PNGs
for (j in seq_along(df_names)) {
  df <- get(df_names[j])
  label <- df_labels[j]
  
  for (i in 1:10) {
    chr_data <- df[df$CHROM == as.character(i), ]
    
    p <- ggplot(chr_data, aes(x = BIN_START, y = PI)) +
      geom_point() +
      theme_classic() +
      ggtitle(paste("Treatment:", label, "- Chromosome", i)) +
      labs(x = "BIN_START", y = "PI") +
      ylim(global_ymin, global_ymax)
    
    filename <- paste0("PI_", label, "_chr", i, ".png")
    ggsave(filename = filename, plot = p, width = 8, height = 5, dpi = 300)
  }
}

```


# Runs of homozygosity

Runs of homozygosity (ROH) are contiguous lengths of homozygous genotypes that are present in an individual due to parents transmitting identical haplotypes to their offspring.

The potential of predicting or estimating individual autozygosity for a subpopulation is the proportion of the autosomal genome above a specified length, termed Froh.

This technique can be used to identify the genomic footprint of inbreeding in conservation programs, as organisms that have undergone recent inbreeding will exhibit long runs of homozygosity. The effect of inbreeding in the resulting sub-populations could be studied by measuring the runs of homozygosity in different individuals.

## Start ROH workflow

```{bash}
vcftools --vcf SNP.TRSdp10g1.FIL.vcf --LROH --out ROD.CADO.all.LROH
```

